FastGapFill: efficient gap filling in metabolic networks.
Ontology highlight
ABSTRACT:
SUBMITTER: Thiele I
PROVIDER: S-EPMC4147887 | biostudies-other | 2014 Sep
REPOSITORIES: biostudies-other
Publications
fastGapFill: efficient gap filling in metabolic networks.
Thiele Ines I Vlassis Nikos N Fleming Ronan M T RM
Bioinformatics (Oxford, England) 20140507 17
<h4>Motivation</h4>Genome-scale metabolic reconstructions summarize current knowledge about a target organism in a structured manner and as such highlight missing information. Such gaps can be filled algorithmically. Scalability limitations of available algorithms for gap filling hinder their application to compartmentalized reconstructions.<h4>Results</h4>We present fastGapFill, a computationally efficient tractable extension to the COBRA toolbox that permits the identification of candidate mis ...[more]
Similar Datasets
Project description:Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.
| S-EPMC5302834 | biostudies-literature
Project description:BackgroundReaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model. Both gap-filling exercises were applied to the same genome-derived qualitative metabolic reconstruction for Bifidobacterium longum subsp. longum JCM 1217, and to the same modeling conditions - anaerobic growth under four nutrients producing 53 biomass metabolites.ResultsThe solution computed by the gap-filling program GenDev contained 12 reactions, but closer examination showed that solution was not minimal; two of the twelve reactions can be removed to yield a set of ten reactions that enable model growth. The manually curated solution contained 13 reactions, eight of which were shared with the 12-reaction computed solution. Thus, GenDev achieved recall of 61.5% and precision of 66.6%. These results suggest that although computational gap fillers are populating metabolic models with significant numbers of correct reactions, automatically gap-filled metabolic models also contain significant numbers of incorrect reactions.ConclusionsOur conclusion is that manual curation of gap-filler results is needed to obtain high-accuracy models. Many of the differences between the manual and automatic solutions resulted from using expert biological knowledge to direct the choice of reactions within the curated solution, such as reactions specific to the anaerobic lifestyle of B. longum.
| S-EPMC6006690 | biostudies-literature
Project description:The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.
| S-EPMC8584699 | biostudies-literature
Project description:Much work has demonstrated that speakers of verb-final languages are able to construct rich syntactic representations in advance of verb information. This may reflect general architectural properties of the language processor, or it may only reflect a language-specific adaptation to the demands of verb-finality. The present study addresses this issue by examining whether speakers of a verb-medial language (English) wait to consult verb transitivity information before constructing filler-gap dependencies, where internal arguments are fronted and hence precede the verb. This configuration makes it possible to investigate whether the parser actively makes representational commitments on the gap position before verb transitivity information becomes available. A key prediction of the view that rich pre-verbal structure building is a general architectural property is that speakers of verb-medial languages should predictively construct dependencies in advance of verb transitivity information, and therefore that disruption should be observed when the verb has intransitive subcategorization frames that are incompatible with the predicted structure. In three reading experiments (self-paced and eye-tracking) that manipulated verb transitivity, we found evidence for reading disruption when the verb was intransitive, although no such reading difficulty was observed when the critical verb was embedded inside a syntactic island structure, which blocks filler-gap dependency completion. These results are consistent with the hypothesis that in English, as in verb-final languages, information from preverbal noun phrases is sufficient to trigger active dependency completion without having access to verb transitivity information.
| S-EPMC4392588 | biostudies-literature
Project description:BackgroundThe iJO1366 reconstruction of the metabolic network of Escherichia coli is one of the most complete and accurate metabolic reconstructions available for any organism. Still, because our knowledge of even well-studied model organisms such as this one is incomplete, this network reconstruction contains gaps and possible errors. There are a total of 208 blocked metabolites in iJO1366, representing gaps in the network.ResultsA new model improvement workflow was developed to compare model based phenotypic predictions to experimental data to fill gaps and correct errors. A Keio Collection based dataset of E. coli gene essentiality was obtained from literature data and compared to model predictions. The SMILEY algorithm was then used to predict the most likely missing reactions in the reconstructed network, adding reactions from a KEGG based universal set of metabolic reactions. The feasibility of these putative reactions was determined by comparing updated versions of the model to the experimental dataset, and genes were predicted for the most feasible reactions.ConclusionsNumerous improvements to the iJO1366 metabolic reconstruction were suggested by these analyses. Experiments were performed to verify several computational predictions, including a new mechanism for growth on myo-inositol. The other predictions made in this study should be experimentally verifiable by similar means. Validating all of the predictions made here represents a substantial but important undertaking.
| S-EPMC3423039 | biostudies-literature
Project description:MOTIVATION:The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions. RESULTS:Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the cost for reaction R is based on the frequency with which R occurs in other organisms within the phylum of M (in this case, Proteobacteria). The assumption behind this method is that different taxonomic groups are biased toward using different metabolic reactions. Evaluation of the new gap-filler on randomly degraded variants of the EcoCyc metabolic model for E.coli showed an increase in the average F1-score to 99.0 (when using the variable weights by frequency method at the phylum level), compared to 91.0 using the previous MetaFlux gap-filler and 80.3 using a basic gap-filler. Evaluation on two other microbial metabolic models showed similar improvements. AVAILABILITY AND IMPLEMENTATION:The Pathway Tools software (including MetaFlux) is free for academic use and is available at http://pathwaytools.com. Additional code for reproducing the results presented here is available at www.ai.sri.com/pkarp/pubs/taxgap/supplementary.zip. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC7523652 | biostudies-literature
Project description:Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.
| S-EPMC4199484 | biostudies-literature
Project description:Investigating the dynamics and function of large-scale spiking neuronal networks with realistic numbers of synapses is made possible today by state-of-the-art simulation code that scales to the largest contemporary supercomputers. However, simulations that involve electrical interactions, also called gap junctions, besides chemical synapses scale only poorly due to a communication scheme that collects global data on each compute node. In comparison to chemical synapses, gap junctions are far less abundant. To improve scalability we exploit this sparsity by integrating an existing framework for continuous interactions with a recently proposed directed communication scheme for spikes. Using a reference implementation in the NEST simulator we demonstrate excellent scalability of the integrated framework, accelerating large-scale simulations with gap junctions by more than an order of magnitude. This allows, for the first time, the efficient exploration of the interactions of chemical and electrical coupling in large-scale neuronal networks models with natural synapse density distributed across thousands of compute nodes.
| S-EPMC7214808 | biostudies-literature
Project description:Although the primary function of DNA polymerase (pol) β is associated with gap-filling DNA synthesis as part of the DNA base excision repair pathway, translesion synthesis activity has also been described. To further understand the potential role of pol β-catalyzed translesion DNA synthesis (TLS) and the structure-function relationships of specific residues in pol β, wild-type and selected mutants of pol β were used in TLS assays with DNA substrates containing bulky polycyclic aromatic hydrocarbon-adducted oligonucleotides. Stereospecific (+) and (-)-anti-trans-(C(10)S and C(10)R) benzo[a]pyrene-7,8-dihydrodiol-9-10-epoxide (BPDE) adducts were covalently attached to both the N(6)-adenine and N(2)-guanine in the major and minor grooves, respectively. For all substrates tested, the presence of the BPDE adducts greatly decreased the efficiency of nucleotide incorporation opposite the lesion, and the stereochemistry of the adducts also further modulated the efficiency of the insertion step, such that lesions which were oriented in the 3' direction relative to the approaching polymerase were considerably more blocking than those oriented in the 5' direction. In the absence of a downstream DNA strand, the extension step beyond the adduct was extremely inefficient, relative to a dinucleotide gap-filling reaction, such that in the presence of the downstream DNA, dinucleotide incorporation was strongly favored. In general, analyses of the TLS activities of four pol β mutants revealed similar overall properties, but wild-type pol β exhibited more than 50-fold greater extension and bypass of the C(10)S-dA adducts as compared to a low fidelity mutant R283K expected to interact with the templating base. Replication bypass investigations were further extended to include analyses of HIV-1 reverse transcriptase, and these studies revealed patterns of inhibition very similar to that observed for pol β.
| S-EPMC3523550 | biostudies-other
Project description:A new strategy for oncology care delivery that includes increasing the numbers and expanding the roles of nonphysician practitioners is critically important to meet the current and potential cancer care needs of the US population.
| S-EPMC2805339 | biostudies-literature